By Dr. Mehul Patel
India’s oil and gas pipeline grid is a national backbone, and corrosion is its quietest threat. For decades, cathodic protection (CP)—and specifically pipe-to-soil potential (PSP) monitoring—has been our principal defence. Regulations already tell operators what to do and how often to do it. But in practice, PSP monitoring still leans on periodic surveys, manual reviews and delayed interventions. That gap between regulatory intent and operational reality is precisely where AI—and now Agentic AI—can help.
The compliance anchor already exists
India’s rulebook is not the problem. PNGRB’s Technical Standards and Specifications including Safety Standards (T4S) for product and natural gas pipelines prescribe PSP measurement cadence (from fortnightly checks at feeding points to quarterly “ON” and annual “instant OFF” PSP at test posts), diagnostic surveys such as CIPS/DCVG at defined intervals, and protection criteria (e.g., polarized potential ≥ –0.85 V vs Cu/CuSO₄, tighter where SRB risk exists, while avoiding over-protection beyond about –1.2 V). BIS standards (IS 8062 for CP; IS 10221 for coatings) and ISO 15589-1 provide the technical spine for design, measurement and documentation. In short, the “what” is settled; it’s the “how—at scale, continuously, and with audit confidence” that needs a rethink.
From reactive to predictive
Traditional programs often find anomalies after trends drift. AI changes that. Trained on multi-year PSP histories, rectifier outputs, soil resistivity, coating survey results, interference corridors and weather, predictive models flag where protection margins are thinning well before they breach thresholds. Statistical drift and seasonality become signals, not noise. Anomaly detectors learn site-specific signatures, reducing false alarms and focusing attention where risk—and consequence—are highest.
What Agentic AI adds
Conventional AI predicts; Agentic AI acts within guardrails. Think of autonomous “digital field engineers” that:
Plan and orchestrate PSP measurements to match T4S cadence (fortnightly/quarterly/annual/five-year), push work orders to field teams, and reconcile time-stamped readings against test-post GIS.
Validate compliance in real time, evaluating PSP against regulatory criteria, and escalating only when sustained deviations occur.
Recommend rectifier set-point adjustments, then trigger post-adjustment verification runs to confirm recovery.
Compile audit-ready dossiers—immutable, time-stamped evidence mapped clause-by-clause to PNGRB/BIS/ISO—for swift regulator or third-party review.
A Make-in-India opportunity
India already uses GPS-tagged PSP loggers, SCADA, and mobile forms across OMCs and CGD networks. The ingredients for intelligence are in place. What’s needed is a policy-aligned architecture: a machine-readable rule engine encoding PNGRB criteria and BIS/ISO methods; secure data pipelines from field to cloud; role-based dashboards for integrity, operations and HSSE; and clear human-in-the-loop boundaries for any automated adjustments. Crucially, the aim is not to change standards but to operationalise them—faithfully and faster.
Cost, risk and credibility
The economics are compelling. Even modest reductions in unprotected exposure time and unnecessary site revisits can pay for the platform. More important is risk reduction: early detection of CP loss at road/rail crossings, river beds, HVDC interference zones or MIC-prone segments cuts the tail-risk of leaks and environmental harm. And when audits arrive, having clause-mapped, time-stamped PSP evidence turns compliance from a scramble into a show-and-tell.
Guardrails and governance
Two cautions. First, data integrity is everything: calibrations, reference electrode health, temperature corrections and IR-drop considerations must be handled correctly or the smartest model will give the wrong answer. Second, agentic autonomy needs strict change controls—clear limits on rectifier adjustments, mandatory human approval for high-impact actions, and complete logs of who did what, when and why. Done right, Agentic AI enhances—not replaces—expert oversight.
The policy ask
Regulators don’t need to rewrite standards. A light-touch circular encouraging digital, continuous, and audit-ready CP programs—fully aligned to existing T4S and BIS/ISO—would catalyse adoption. Pilot corridors under supervision of PNGRB/PSUs can set templates for nationwide scale-up, including guidance on cybersecurity, data retention and third-party validation.
Bottom line: AI and Agentic AI won’t replace CP engineers; they’ll give them superpowers—turning a compliance obligation into a real-time assurance system that keeps India’s pipelines safer, cleaner and more reliable.
Five quick wins to deploy Agentic AI for CP
1. Digitise the rulebook: Encode PNGRB T4S thresholds, cadences and documentation needs into a policy engine.
2. Fix the data plumbing: Stream PSP (ON/OFF), rectifier currents, soil/chemistry and CIPS/DCVG results with GPS/time/user metadata.
3. Start with prediction, not autonomy: Pilot hotspot prediction on one corridor; validate against field findings.
4. Add agentic workflows carefully: Let agents schedule, validate and draft work orders; keep humans approving rectifier changes.
5. Make audits one-click: Auto-generate clause-mapped reports (PNGRB/BIS/ISO) with immutable logs and evidence.
Author: Dr. Mehul Patel is Chief Operating Officer (ELV-AI-IT Infra Division) at Vectras Enprocon Ltd., working on applied AI for critical infrastructure, safety and compliance. Views are personal.